# 4.2. Before starting¶

In the section The Cosmology module we learned how to use EPIC to set up a cosmological model and load some datasets. The next logical step is to calculate the probability density at a given point of the parameter space, given that model and according to the chosen data. This can be done as follows:

In this example I am choosing the cosmic chronometers dataset, the Hubble constant local measurement and the simplified version of the JLA dataset. The Analysis object is created from the dictionary of datasets, the model and a dictionary of priors in the model parameters (including nuisance parameters related to the data). The probability density at any point can then be calculated with the module log_posterior, which returns the logarithm of the posterior probability density and the logarithm of the likelihood. Setting the option chi2 to True (It is False by default) makes the calculation of the likelihood as $$\log \mathcal{L} = - \chi^2/2$$, dropping the usual multiplicative terms from the normalized Gaussian likelihood. When false, the results include the contribution of the factors $$1/\sqrt{2\pi} \sigma_i$$ or the factor $$1/\sqrt{2 \pi |\textbf{C}|}$$. These are constant in most cases, making no difference to the analysis, but in other cases, depending on the data set, the covariance matrix $$\textbf{C}$$ can depend on nuisance parameters and thus vary at each point.

Now that we know how to calculate the posterior probability at a given point, we can perform a Monte Carlo Markov Chain simulation to assess the confidence regions of the model parameters. The main script epic.py accomplishes this making use of the objects and modules here presented.

The configuration of the analysis (choice of model, datasets, priors, etc) is defined in a .ini configuration file that the program reads. The program creates a folder in the working directory with the same name of this .ini file, if it does not already exist. Another folder is created with the date and time for the output of each run of the code, but you can always continue a previous run from where it stopped, just giving the folder name instead of the .ini file. The script is stored in the EPIC source folder, where the .ini files should also be placed. The default working directory is the EPIC’s parent directory, i.e., the epic repository folder.

## Changing the default working directory¶

By default, the folders with the name of the .ini files are created at the repository root level. But the chains can get very long and you might want to have them stored in a different drive. In order to set a new default location for all the new files, run:

$python define_altdir.py  This will ask for the path of the folder where you want to save all the output of the program and keep this information in a file altdir.txt. If you want to revert this change you can delete the altdir.txt file or run again the command above and leave the answer empty when prompted. To change this directory temporarily you can use the argument --alt-dir when running the main script. ## The structure of the .ini file¶ Let us work with an example, with a simple flat $$\Lambda\text{CDM}$$ model. Suppose we want to constrain its parameters with $$H(z)$$, supernovae data, CMB shift parameters and BAO data. The model parameters are the reduced Hubble constant $$h$$, the present-day values of the physical density parameters of dark matter $$\Omega_{c0} h^2$$, baryons $$\Omega_{b0} h^2$$ and radiation $$\Omega_{r0} h^2$$. We will not consider perturbations, we are only constraining the parameters at the background level. Since we are using supernovae data we must include a nuisance parameter $$M$$, which represents a shift in the absolute magnitudes of the supernovae. Use of the full JLA catalogue requires the inclusion of the nuisance parameters $$\alpha$$, $$\beta$$ and $$\Delta M$$ from the light-curve fit. The first section of .ini is required to specify the type of the model, whether to use physical density parameters or not, and which species has the density parameter derived from the others (e. g. from the flatness condition): [model] type = lcdm physical = yes optional species = ['baryons', 'radiation'] derived = lambda  The lcdm model will always have the two species cdm and lambda. We are including the optional baryonic fluid and radiation, which being a combined species replaces photons and neutrinos. The configurations and options available for each model are registered in the EPIC/cosmology/model_recipes.ini file. This section can still received the interaction setup dictionary to set the configuration of an interacting dark sector model. Details on this are given in the previous section Interacting Dark Energy models. The second section defines the analysis: a label, datasets and specifications about the priors ranges and distributions. The optional property prior distributions can receive a dictionary with either Flat or Gaussian for each parameter. When not specified, the code will assume flat priors by default and interpret the list of two numbers as an interval prior range. When Gaussian, these numbers are interpreted as the parameters $$\mu$$ and $$\sigma$$ of the Gaussian distribution. In the simulation section, we specify the parameters of the diagonal covariance matrix to be used with the proposal probability distribution in the sampler. Values comparable to the expected standard deviation of the parameter distributions are recommended. [analysis] label =$H(z)$+$H_0\$ + SNeIa + BAO + CMB
datasets = {
'Hz':   'cosmic_chronometers',
'H0':   'HST_local_H0',
'SNeIa': 'JLA_simplified',
'BAO':   [
'6dF+SDSS_MGS',
'SDSS_BOSS_CMASS',
'SDSS_BOSS_LOWZ',
'SDSS_BOSS_QuasarLyman',
'SDSS_BOSS_consensus',
'SDSS_BOSS_Lyalpha-Forests',
],
'CMB':   'Planck2015_distances_LCDM',
}
priors = {
'Och2' :  [0.08, 0.20],
'Obh2' :  [0.02, 0.03],
'h' : [0.5, 0.9],
'M' : [-0.3, 0.3],
}
prior distributions =
fixed = {
'T_CMB' : 2.7255
}

[simulation]
proposal covariance = {
'Och2' : 1e-3,
'Obh2' : 1e-5,
'h' : 1e-3,
'M': 1e-3,
}